{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ggplot: (285097725)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YnQ0kj2YbAABMitnZQGpotgEAwHUxOxtIHc02AACYELOzgRtHsw0AAMZhdjYwNWi2AQBA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\nGwT376dvtNqGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe5aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(mpg, aes(x='cty', y='hwy')) + \\\n",
    "    geom_point() + \\\n",
    "    geom_abline(slope=1, intercept=5, color='steelblue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
